pandas使用merge实现百倍加速的操作

target_dates = pd.date_range(end=now, periods=100, freq="7D")
full_dates = pd.date_range(start, now).tolist()
org_dates = df.date.tolist()

last_date = None
for d in full_dates:
 if d in org_dates:
  date_map[d] = d
  last_date = d
 elif last_date is not None:
  date_map[d] = last_date
 else:
  continue
new_df = pd.DataFrame()
for td in target_dates:
 new_df = pd.concat（[new_df, df[df["date"]==date_map[td]]） 

target_dates = pd.date_range(end=now, periods=100, freq="7D")
full_dates = pd.date_range(start, now).tolist()
org_dates = df.date.tolist()

last_date = None
for d in full_dates:
 if d in org_dates:
  date_map[d] = d
  last_date = d
 elif last_date is not None:
  date_map[d] = last_date
 else:
  continue
  
#### main change is from here #####
date_map_list = []
for td in target_dates:
 date_map_list.append({"target_date":td, "org_date":date_map[td]}) 
date_map_df = pd.DataFrame(date_map_list)
new_df = date_map_df.merge(df, left_on=["org_date"], right_on=["date"], how="inner") 

left = pd.DataFrame({'A': [1, 2], 'B': [2, 2]})

right = pd.DataFrame({'A': [4, 5, 6], 'B': [2, 2, 2]})

result = pd.merge(left, right, on='B', how='outer')

pd.merge(left, right, on='B', how='outer', validate="one_to_many")
 
# 打印的结果:
 A_x B A_y
0 1 1 NaN
1 2 2 4.0
2 2 2 5.0
3 2 2 6.0

validate : str, optional
If specified, checks if merge is of specified type.

“one_to_one” or “1:1”: check if merge keys are unique in both left and right datasets.
“one_to_many” or “1:m”: check if merge keys are unique in left dataset.
“many_to_one” or “m:1”: check if merge keys are unique in right dataset.
“many_to_many” or “m:m”: allowed, but does not result in checks.